Scratch Resistance and Damage Mechanisms Arising in Titanium Carbide–Nickel Aluminide-Based Laser DED Clads on D2 Tool Steel
Bibliographic record
Abstract
TiC-based cermet clads were applied onto high-Cr-containing, cold work D2 tool steel substrates through laser-directed energy deposition (L-DED). A novel suspension-based preplacement method was used to apply the feedstock prior to laser cladding. The preplaced material was then subjected to laser processing using various laser powers (200 to 350 W) and scanning speeds (58 to 116 mm/min.), resulting in the fabrication of high-density clads on the substrates. Hardness profiles were generated by cross-sectional micro-indentation of the clad layers. Micro-Vickers hardness (HV) values of the cermet clads were measured from load–displacement curves under a range of applied normal forces, which are in the range of 265.7 to 890.3 HV. As a preliminary assessment of the wear response, a variety of single-pass scratch testing approaches were undertaken. A qualitative evaluation of ‘interface’ mechanics between the ‘clad’ and substrate material was also performed by cross-sectional scratching of the clads; as a chemical clad is developed, this effectively is assessing the transitions through the clad microstructure. Failure modes and damage mechanism were examined at different processing parameters by means of acoustic emission (AE) and coefficient of friction (COF) measurements, together with assessment of the post-test microstructures. The scratch hardness (HSp) of the cermet clads varied within the range of 4.88 to 7.58 GPa, as a function of applied normal force (ranged within 10–40 N), which was considerably higher than the D2 substrate (HSp = 3 GPa).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".